Roman Kazantsev
ab2038041d
It needs to be tested on GPU Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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from common.tf_layer_test_class import CommonTFLayerTest
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# Testing Cast operation (Initial Implementation)
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# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/Cast
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class TestCastOp(CommonTFLayerTest):
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input_type = np.float32
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# Overload inputs generation to fill dummy input
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def _prepare_input(self, inputs_dict):
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for input in inputs_dict.keys():
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inputs_dict[input] = np.random.randint(-256, 256, inputs_dict[input]).astype(self.input_type)
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return inputs_dict
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# input_shape - should be an array
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# input_type - type of input value
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# output_type - type of output value
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# truncate - boolean flag of truncation
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# ir_version - common parameter
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# use_new_frontend - common parameter
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def create_cast_op_placeholder_const_net(self, input_shape, input_type, output_type, truncate, ir_version, use_new_frontend):
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if(input_type == output_type):
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pytest.skip("Input and output types shouldn't be equal")
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import tensorflow as tf
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tf.compat.v1.reset_default_graph()
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self.input_type = input_type
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tf_types = {
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np.int32: tf.int32, np.int64: tf.int64,
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np.float16: tf.float16, np.float32: tf.float32, np.float64: tf.float64
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}
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# Create the graph and model
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with tf.compat.v1.Session() as sess:
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tf_input = tf.compat.v1.placeholder(tf_types[input_type], input_shape, 'Input')
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tf.raw_ops.Cast(x = input_shape, DstT = output_type, Truncate = truncate)
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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ref_net = None
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return tf_net, ref_net
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test_data = [
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pytest.param(
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dict(input_shape=[2, 3]), #Simple test
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marks=pytest.mark.precommit_tf_fe),
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dict(input_shape=[2, 3, 3, 4]), #Simple test with possible nchw/nhwc
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]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("input_type", [ np.int32, np.int64, np.float16, np.float32, np.float64 ])
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@pytest.mark.parametrize("output_type", [ np.int32, np.int64, np.float16, np.float32, np.float64 ])
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@pytest.mark.parametrize("truncate", [ False, True ])
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@pytest.mark.nightly
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def test_cast_op_placeholder_const(self, params, input_type, output_type, truncate, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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self._test(*self.create_cast_op_placeholder_const_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend, input_type=input_type,
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output_type=output_type, truncate=truncate),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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